No abstract

The use of dental morphology to estimate ancestry has a long history within dental anthropology. Over the past two decades methods employing dental morphology within forensic anthropology have become more formalized with the incorporation of statistical models. We present here on a new application (rASUDAS) to estimate ancestry of unknown individuals using crown and root morphology of the dentition. The reference sample is composed of 21 traits based on the Arizona State University Dental Anthropology System and represents approximately 30,000 individuals from seven geographic regions. The statistical program was created in R and uses a naïve Bayes classifier algorithm to assign posterior probabilities for individual group assignment. A random sample of 150 individuals from the dataset was chosen and input into the program. In a sevengroup analysis, the model was correct in group assignment 51.8% of the time. In a four-group analysis, classification improved to 66.7%, and with only three groups considered the accuracy improved to 72.7%. It is still necessary to validate the program using forensic cases and to augment the reference sample with modern skeletal data. However, we present these results as a proof of concept of the statistical application and the use of dental morphology in the estimation of ancestry.

Age at death estimation in adult skeletons is hampered, among others, by the unremarkable correlation of bone estimators with chronological age, implementation of inappropriate statistical techniques, observer error, and skeletal incompleteness or destruction. Therefore, it is beneficial to consider alternative methods to assess age at death in adult skeletons. The decrease in bone mineral density with age was explored to generate a method to assess age at death in human remains. A connectionist computational approach, artificial neural networks, was employed to model femur densitometry data gathered in 100 female individuals from the Coimbra Identified Skeletal Collection. Bone mineral density declines consistently with age and the method performs appropriately, with mean absolute differences between known and predicted age ranging from 9.19 to 13.49 years. The proposed method-DXAGE-was implemented online to streamline age estimation. This preliminary study highlights the value of densitometry to assess age at death in human remains.

The assessment of sex is crucial to the establishment of a biological profile of an unidentified skeletal individual. The best methods currently available for the sexual diagnosis of human skeletal remains generally rely on the presence of well-preserved pelvic bones, which is not always the case. Postcranial elements, including the femur, have been used to accurately estimate sex in skeletal remains from forensic and bioarcheological settings. In this study, we present an approach to estimate sex using two measurements (femoral neck width [FNW] and femoral neck axis length [FNAL]) of the proximal femur. FNW and FNAL were obtained in a training sample (114 females and 138 males) from the Luís Lopes Collection (National History Museum of Lisbon). Logistic regression was used to develop a model to predict sex in unknown individuals. The logistic regression model correctly predicted sex in 85.3% to 85.7% of the cases. The model was also evaluated in a test sample (96 females and 96 males) from the Coimbra Identified Skeletal Collection (University of Coimbra), resulting in a sex allocation accuracy of 80.1% to 86.2%. This study supports the relative value of the proximal femur to estimate sex in skeletal remains, especially when other exceedingly dimorphic skeletal elements are not accessible for analysis.